Department of Climate and Space Sciences and Engineering in the College of Engineering at the University of Michigan

News

Two Climate & Space faculty awarded MICDE Catalyst Grants

Posted: May 10, 2018

Two Climate & Space faculty awarded MICDE Catalyst Grants

Climate & Space is pleased to announce that two faculty members will receive funding for their research projects.

The Michigan Institute for Computational Discovery and Engineering has awarded its second round of Catalyst Grants, providing between $80,000 and $90,000 each to seven innovative projects in computational science. The proposals were judged on novelty, likelihood of success at catalyzing larger programs and potential to leverage ARC’s computing resources.

Associate Professor Christiane Jablonowski leads the "Advancing the Computational Frontiers of Solution-Adaptive, Scale-Aware Climate Models" project. Their research will further develop a 3-D mesh adaptation model for climate modeling, allowing computational resources to be focused on phenomena of interest such as tropical cyclones or other extreme weather events. The project will also introduce data-driven machine learning paradigms into modeling of clouds and precipitation.

 

Structure of a block-structured adaptive grid that overlays a so-called ‘cubed-sphere’ base grid. The atmospheric vortices (in color) resemble idealized tropical cyclones that are captured by the refined grid patches. The adaptations are guided by the location and strength of the rotational motion. Image courtesy Michigan Institute for Computational Discovery & Engineering.


Assistant Research Scientist Darren McKague's research project, "Urban Flood Modeling at “Human Action” Scale: Harnessing the Power of Reduced-Order Approaches and Uncertainty Quantification" will demonstrate urban flood monitoring and prediction capabilities using NASA Cyclone Global Navigation Satellite System (CYGNSS) data and relying on state-of-the-science uncertainty quantification tools in a proof-of-concept urban flooding problem of high complexity.

The problem of flood prediction incurs multiple uncertainties (a) and is of high computational complexity (b). New remote sensing data from the CYGNSS mission (c) can inform complex physically-based simulations (d) but in order to achieve feasibility of real-time solutions and uncertainty quantification, novel approaches are required. This project will develop reduced-order modeling tools (e) as innovative, parsimonious representation of rigorous hydrologic and hydrodynamic model formulations to efficiently obtain probability density distributions of one or many quantities of interest (f). Image courtesy Michigan Institute for Computational Discovery & Engineering.


For more information, see MICDE's wesbite.